Publications

Predicting Multimorbidity in 50,000 People Living with Obesity: A Machine Learning Model Applied to Understand Obesity Progression in Two Health Care Systems Line Egerod, Rikke Linnemann Nielsen, Zahra McVey, Joseph Katigbak, Thomas Monfeuga, Frederik Steensgaard Gade, August T. H. Schreyer, Luis G. Leal, William G. Haynes, Alex Greenfield, Ella Nkhoma, Robert R. Kitchen, Michael L. Wolden, Kasper S. Matthiessen, Laurent Gautier, Abd A. Tahrani, Ramneek Gupta Preprint at SSRN (2025) ...

Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using interpretable machine learning Rikke Linnemann Nielsen, Thomas Monfeuga, Robert R. Kitchen, Line Egerod, Luis G. Leal, August Thomas Hjortshøj Schreyer, Frederik Steensgaard Gade, Carol Sun, Marianne Helenius, Lotte Simonsen, Marianne Willert, Abd A. Tahrani, Zahra McVey & Ramneek Gupta Nature Communications (2024) DOI: 10.1038/s41467-024-46663-4 Abstract Osteoarthritis (OA) is increasing in prevalence and has a severe impact on patients’ lives. However, our understanding of biomarkers driving OA risk remains limited. We developed a model predicting the five-year risk of OA diagnosis, integrating retrospective clinical, lifestyle and biomarker data from the UK Biobank (19,120 patients with OA, ROC-AUC: 0.72, 95%CI (0.71–0.73)). Higher age, BMI and prescription of non-steroidal anti-inflammatory drugs contributed most to increased OA risk prediction ahead of diagnosis. We identified 14 subgroups of OA risk profiles. These subgroups were validated in an independent set of patients evaluating the 11-year OA risk, with 88% of patients being uniquely assigned to one of the 14 subgroups. Individual OA risk profiles were characterised by personalised biomarkers. Omics integration demonstrated the predictive importance of key OA genes and pathways (e.g., GDF5 and TGF-β signalling) and OA-specific biomarkers (e.g., CRTAC1 and COL9A1). In summary, this work identifies opportunities for personalised OA prevention and insights into its underlying pathogenesis. ...

DiscoTope-3.0: Improved B-cell epitope prediction using inverse folding latent representations Magnus Haraldson Høie, Frederik Steensgaard Gade, Julie Maria Johansen, Charlotte Würtzen, Ole Winther, Morten Nielsen, Paolo Marcatili Frontiers in Immunology (2024) DOI: 10.3389/fimmu.2024.1322712 Abstract Accurate computational identification of B-cell epitopes is crucial for the development of vaccines, therapies, and diagnostic tools. Structure-based prediction methods generally outperform sequence-based models, but are limited by the availability of experimentally solved structures. Here, we present DiscoTope-3.0, a B-cell epitope prediction tool that exploits inverse folding representations from solved or AlphaFold-predicted structures. On independent datasets, the method demonstrates improved performance on both linear and non-linear epitopes with respect to current state-of-the-art algorithms. Most notably, our tool maintains high predictive performance across solved and predicted structures, alleviating the need for experiments and extending the general applicability of the tool by more than 4 orders of magnitude. DiscoTope-3.0 is available as a web server and downloadable package, processing up to 50 structures per submission. The web server interfaces with RCSB and AlphaFoldDB, enabling large-scale prediction on all currently cataloged proteins. DiscoTope-3.0 is available here and on BioLib. ...